Causality in unblinded randomised community trials.

Nevil Pierse – PhD 2011

The reliable identification of causal relationships is vital for sound policy making. It is particularly important when a policy requires large amounts of resources to be allocated.Â However, establishing causality is often difficult, especially in the home/community environment.Â These are cases where, for example, an intervention is made by modifying some aspect of the home environment, and the impact of that intervention on the residentsâ€™ lives is to be measured.

The gold standard for identifying causal relationships is provided by randomised control trials, in which both the researchers and the participants are unaware of (â€˜blindedâ€™ to) who is in the treatment group (who receive the intervention) and who is in the control group (who do not).Â However, in randomised community trials it is almost always impossible to blind the participants to the intervention taking place, and community trials have been subject to criticism for this reason.

For example, in the recently completed Housing, Heating and Health Study, a random sample of households were provided with better heating methods, and a control group left untouched.Â Participants were very aware whether or not they had a new form of heating installed. This awareness potentially changed their behaviours and questionnaire responses in a way that was unrelated to the health benefits of provision of efficient heat, which was the focus of the intervention. Such changes in behaviour and response by the people in the intervention group, called the â€œplaceboâ€ effect, can lead to uncertainty about the actual effect of the intervention.

A rigorous statistical treatment of the placebo and other confounding effects has not yet been applied to unblinded community trials. This research project proposes to do this.Â We will examine new methods of adjusting for the placebo effect, in particular we will apply statistical techniques such as instrumental variables and Bayesian hierarchical modelling which offer great potential to adjust for the biases introduced by these effects.Â This work will be a significant contribution to the literature on the analysis of unblinded community trials.